A system designed to automate the process of attendance management. It comprises of various modules consisting detection and recognition of faces take from web cam/ IP cameras.
Complete UI has been designed with PyQT. A tool to write UI with raw python code.
Eigenfaces is the name given to a set of eigenvectors when they are used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face classification.The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images. The eigenfaces themselves form a basis set of all images used to construct the covariance matrix. This produces dimension reduction by allowing the smaller set of basis images to represent the original training images. Classification can be achieved by comparing how faces are represented by the basis set.
Let be a random vector with observations .
- Compute the mean
- Compute the the Covariance Matrix S
- Compute the eigenvalues and eigenvectors.`
- Order the eigenvectors descending by their eigenvalue. The principal components are the eigenvectors corresponding to the largest eigenvalues.
- The principal components of the observed vector.
- Reconstruction from the PCA.
The Eigen faces method then performs face recognition by:
- Projecting all training samples into the PCA subspace.
- Projecting the query image into the PCA subspace.
- Finding the nearest neighbour between the projected training images and the projected query image.
Still there’s one problem left to solve. Imagine we are given 400 images sized 100 X 100 pixel. The Principal Component Analysis solves the covariance matrix S = XX^T , where SIZE(X) = 1000 X 400 in our example. You would end up with a 10000 X 10000 matrix, roughly 0.8 GB . Solving this problem isn’t feasible, so we’ll need to apply a trick. From your linear algebra lessons you know that a matrix with can only have non-zero eigenvalues. So it’s possible to take the eigenvalue decomposition of size instead:
and get the original eigenvectors of with a left multiplication of the data matrix:
The resulting eigenvectors are orthogonal, to get orthonormal eigenvectors they need to be normalized to unit length. I don’t want to turn this into a publication, so please look into [Duda01] for the derivation and proof of the equations.
Being one of the most successful applications of the image processing, face recognition has a vital role in technical field especially in the field of security purpose. Human face recognition is an important field for verification purpose especially in the case of student’s attendance. This paper is aimed at implementing a digitized system for attendance recording. Current attendance marking methods are monotonous & time consuming. Manually recorded attendance can be easily manipulated. Hence the paper is proposed to tackle all these issues. Face Detection and Recognition is an important area in the field of substantiation. Maintenance of records of students along with monitoring of class attendance is an area of administration that requires significant amount of time and efforts for management. Intelligent Attendance Management System performs the daily activities of attendance analysis, for which face recognition is an important aspect. The prevalent techniques and methodologies for detecting and recognizing face like PCA-LDA, etc. fail to overcome issues such as scaling, pose, illumination, variations, rotation, and occlusions. The proposed system provides features such as detection of faces, extraction of the features, detection of extracted features, analysis of students' attendance and monthly attendance report generation. The proposed system integrates techniques such as image contrasts, integral images features and cascading classifier for feature detection. Faces are recognized using advanced Eigen face algorithm using the database that contains images of students and is used to recognize student using the captured image. Better accuracy is attained in results and the system takes into account the change that occurs in the face over the period of time.